✨ feat: notes and CT reconstruction data from latest weeks

The CT data is super interesting; most definitely worth going through.

.editorconfig

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 indent_size = 4
 trim_trailing_whitespace = false
 
+[*.typ]
+indent_style = space
+indent_size = 2
+
 [*.yml]
 indent_style = space

.gitattributes

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 *.pdf filter=lfs diff=lfs merge=lfs -text
 *.odt filter=lfs diff=lfs merge=lfs -text
 *.doc filter=lfs diff=lfs merge=lfs -text
+*.ppt filter=lfs diff=lfs merge=lfs -text
 *.pptx filter=lfs diff=lfs merge=lfs -text
 *.docx filter=lfs diff=lfs merge=lfs -text
+*.xlsx filter=lfs diff=lfs merge=lfs -text
 
 # Image
 *.jpg filter=lfs diff=lfs merge=lfs -text
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 *.tga filter=lfs diff=lfs merge=lfs -text
 *.aif filter=lfs diff=lfs merge=lfs -text
 *.ttf filter=lfs diff=lfs merge=lfs -text
+*.tif filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
 
 # Audio
 *.mp3 filter=lfs diff=lfs merge=lfs -text
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 *.a filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.mat filter=lfs diff=lfs merge=lfs -text
+
+*.raw filter=lfs diff=lfs merge=lfs -text
+*.vvi filter=lfs diff=lfs merge=lfs -text

.python-version

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+3.13

main.py

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+def main():
+    print("Hello from materials-characterization!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

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+[project]
+name = "materials-characterization"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "fastexcel>=0.13.0",
+    "hvplot>=0.11.2",
+    "ipykernel>=6.29.5",
+    "jupyter-bokeh>=4.0.5",
+    "matplotlib>=3.10.1",
+    "numpy>=2.2.4",
+    "polars>=1.26.0",
+    "pyarrow>=19.0.1",
+    "sympy>=1.13.3",
+]

ref/bookmarks.md

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+- https://materialhub.org/
+- https://github.com/ncfrey/resources
+- https://fair-chem.github.io/core/datasets/odac.html
+- https://mattermodeling.stackexchange.com/questions/340/is-there-any-complete-list-of-open-source-hubs-specifically-designed-for-materia
+- https://materials.registry.nist.gov/
+- https://tpsx.arc.nasa.gov/
+- https://pypi.org/project/steelpy/
+- https://openmaterialsdb.se
+
+# Continuum Modelling
+- https://www.nature.com/articles/nmat3874
+- https://www.nature.com/articles/nmat3825
+

weeks/week_5/notes.typ

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+#import "@preview/theorion:0.3.2": *
+#import "@preview/lovelace:0.3.0": *
+#import "@preview/physica:0.9.4": *
+#import "@preview/unify:0.7.1": num, qty, numrange, qtyrange
+#import "@preview/cetz:0.3.1": *
+#import "@preview/typsium:0.2.0": ce
+
+#import "@local/rosetypst:0.1.0": *
+
+#import cosmos.rainbow: *
+#show: show-theorion
+
+#set math.vec(delim: "[")
+#set math.mat(delim: "[")
+
+#set-inherited-levels(1)
+#set-theorion-numbering("1.1")
+
+#set page(
+  paper: "a4",
+  columns: 2,
+  margin: 1.5cm,
+)
+#set par(leading: 0.55em, justify: true)
+#set text(10pt, font: "New Computer Modern")
+#set heading(numbering: "1.1")
+#show heading: set block(above: 1.4em, below: 1em)
+
+#let first_heading = state("first_heading", true)
+#show heading.where(level: 1): it => [
+  #if not first_heading.get() {
+    pagebreak()
+  } else {
+    first_heading.update(false)
+  }
+  #place(
+    top + center,
+    scope: "parent",
+    float: true,
+    text()[
+      #smallcaps(it)
+    ],
+  )
+]
+
+
+
+= X-Ray Imaging
+== Multiscale Characterization
+Diffraction with X-Rays is very good at looking into things like:
+- Batteries
+- Layered Things
+- Polycrystalline Structures
+- Grains
+- Dislocations in Materials
+- Inside of Molecules
+
+For instance, it is possible to image a sample of cast aluminum alloy and deduce where the void defects are.
+
+_This can then be compared to models of stresses and strains!_
+
+#example[
+  It's important to be able to characterize the evolution of cracks in wind turbines.
+
+  Using X-Ray tomography, this can be directly examined in 3D.
+]
+
+
+
+== X-Ray Radiology
+The classical thing about radiography is that depth is difficult
+
+#definition[
+  The #term[linear attenuation coefficient] is termned as
+
+  $ dv(I, x) = -mu(x) I(x) $
+
+  which solves to:
+
+  $ -ln(I_"det" / I_0) = integral_"path" mu(x) d x $
+
+  #[
+    #set align(center)
+    #canvas({
+      import draw: *
+
+      let pnt_source = (0,0)
+      let pnt_sample = (2.5,0)
+      let pnt_detector = (5,0)
+
+      circle(pnt_source, radius: (0.25, 0.5), fill: blue.transparentize(50%), stroke: (dash: "dashed"))
+      circle(pnt_sample, radius: (0.25, 0.5), fill: none, stroke: gray)
+      circle(pnt_detector, radius: (0.25, 0.5), fill: green, stroke: green)
+
+      line(pnt_source, pnt_detector, mark: (end: ">"))
+
+
+      content(pnt_source, anchor: "north", padding: 0.6, $"Source"$)
+      content(pnt_sample, anchor: "north", padding: 0.6, $"Sample"$)
+      content(pnt_detector, anchor: "north", padding: 0.6, $"Detector"$)
+    })
+  ]
+]
+
+#definition[
+  The #term[attenuation coefficient]
+
+  $ mu prop sum_i (Z_i^4 rho_i) / E^3 $
+
+]
+
+
+
+== Radiography Setup
+The principle is to sweep, then record angular deviations.
+
+#todo[More detail]
+
+
+
+== Algebraic Reconstruction
+
+Consider a 2D slice of a voxel grid:
+#[
+  #set align(center)
+  #canvas(length: 0.5cm, {
+    import draw: *
+    
+    draw_xy_axes(x: 11, y: 11)
+    grid((0,0), (10,10), step: 1)
+  })
+]
+
+$ mb(M) vb(f) = vb(p) $
+
+...
+
+
+
+== Filtered Backprojection
+In most commercial instruments, a Fourier Transform-based approach is used.
+
+1. A sample is recorded as $P_theta (t)$.
+2. This is "projected" to $vecrow(t, theta)$ chart is recorded.
+3. The fourier transform of the $vecrow(t, theta)$ is computed, producing a $vecrow(omega, theta)$ diagram.
+4. This is reconstructed back to a $vecrow(u, v)$ fourier space.
+
+
+
+== Discrete Tomography
+#todo[??]
+
+
+
+== Synchrotrons
+You build a large circle, and accelerate electrons to near the speed of light with large electromagnets.
+
+They become pulsed, naturally.
+
+When they travel sufficienly fast, the bending of the electrons cause the emission of very powerful, very parallel X-Rays.
+
+=== Brilliance
+How many useful X-Rays are there?
+
+$ "brilliance" = "photons" / ("mrad"^2 dot "mm"^2 dot 0.1% dot "sec") $
+
+These days, modern synchrotrons can reach a brilliance of $10^21$.
+
+What can be done with more x-rays?
+
+/ Speed: Hard things can be imaged faster - even to the point of real-time.
+/ Materials: Very dense things can be imaged faster.
+
+#example[
+  *Speed*: They were able to make a 3D movie of a fly, where it barely manages to flap its wings between frames.
+
+  Aka. they can see inside of the fly, including colors of ex. the muscles, the moving organs, etc. .
+]
+
+#example[
+  *Resolution*. They can image down to #qty("5", "nm") now!
+
+  _Electrons are still, with their theoretical resolution of #qty("5", "angstrom")._
+]
+
+== Phase Contrast Tomography
+Consider the incoming wave.
+Not only the "shadow" aka. absorption can be used; the phase, too, is available.
+
+As it turns out.
+
+#example[
+  Smallest detectable hole at #qty("25", "keV") is:
+  / Absorption: #qty("20", "um")
+  / Phase: #qty("0.05", "um")
+]
+
+== Diffraction Contrast Tomography
+Finally, when studying crystals, the diffraction can also be measured to reconstruct the internal structure.

weeks/week_5/scan/2025-03-13-test-4/info.txt

@@ -0,0 +1,6 @@
+[Data]
+Experiment name:  = "2025-03-13-test-4"
+Author = "Sofus"
+Institute = "Fysik"
+Lecture ID = "10031"
+Protected = FALSE